Iversion of normalized difference vegetation index based on GPS-IR
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摘要: 针对利用GPS接收机在接收L 波段信号时对周围植被水分含量较为敏感的特性，使用GPS反射信号的变化，进行测站归一化植被指数(NDVI)反演. 利用2个GPS参考站近5年的连续观测数据计算的归一化微波反射指数(NMRI)，构建了反演NDVI的一元线性模型. NMRI整体变化趋势与同时间段内中分辨率成像光谱仪(MODIS) NDVI趋势表现一致，其反演结果相关系数R分别为0.626 53、0.625 73，均方根误差(RMSE)分别为0.051 29和0.055 08，进而使用BP神经网络模型反演相关系数分别提高了2%、6%. 表明GPS干涉反射测量(GPS-IR)反演区域NDVI结果具有较高可靠性. 该研究为获取精确位置、实时连续、高分辨率的 NDVI 提供了一定的理论支撑.
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关键词:
- GPS干涉反射测量(GPS-IR) /
- 归一化植被指数(NDVI) /
- 归一化微波反射指数(NMRI) /
- 指数反演
Abstract: In view of the fact that GPS receiver is sensitive to the water content of surrounding vegetation when receiving L-band signal, the normalized difference vegetation index (NDVI) of the station is retrieved by using the change of GPS reflected signal. In this paper, the normalized microwave reflection index (NMRI) calculated from the continuous observation data of two GPS reference stations in recent five years is used to construct a univariate linear model for NDVI inversion. The correlation coefficients R of the inversion results are 0.626 53 and 0.625 73 respectively, and the root mean square error root mean square error (RMSE) are 0.051 29 and 0.055 08 respectively. The correlation coefficients of the inversion results are increased by 2% and 6% respectively by using BP neural network model, which indicates that the regional NDVI inversion results of GPS-interferometric reflectometry (GPS-IR) have high reliability. This study provides a theoretical support for obtaining accurate position, real-time continuous and high-resolution NDVI. -
表 1 GPS 参考站概况
(°) 测站名 概略位置 纬度 经度 P041 Boulder, CO 39.949492 254.805734 P049 Great Falls, MT 47.349964 249.093768 
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表 2 回归分析结果
测站 回归模型 相关系数 RMSE P041 y=0.19675+1.79965x 0.744 28 0.051 29 P049 y=0.17992+2.2808x 0.734 96 0.055 08
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